A within-subject study of 12 developers found that security training reduced validated weaknesses by 31.5% and critical issues by 79.2% in LLM-assisted backend coding.
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10 Pith papers cite this work. Polarity classification is still indexing.
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ORBIT achieves 100% compilation success and 91.7% test success on 24 mostly large programs from CRUST-Bench by using dependency-aware orchestration and iterative verification, outperforming prior static and baseline tools.
SafeTrans achieves up to 80% successful C-to-Rust translations via LLM iterative repair on 2653 programs and two real projects, with some C vulnerabilities carrying over to the Rust output.
An off-the-shelf LLM prompted on tokenized Modbus traffic from public ICS datasets matches supervised baselines in normal-versus-critical classification accuracy while generating token-grounded audit records without any model updates.
A six-month ethnographic co-creation project in a real SOC demonstrates that practitioner involvement in LLM tool design can overcome typical adoption barriers in cybersecurity operations.
LanG presents a governance-aware agentic AI platform for unified security operations that reports strong performance on incident correlation, rule generation, attack reconstruction, and AI safety guardrails in an open-source package.
SentinelSphere integrates an AI threat detector using an enhanced DNN on benchmark datasets with a fine-tuned quantized LLM for user training and awareness.
An integrated framework using autoencoders, deep reinforcement learning, and LLMs automates risk-based prioritization and contextual analysis of suspicious network traffic within Splunk SOC environments.
Sentra-Guard reports 99.96% detection of adversarial LLM prompts with AUC 1.00 and ASR of 0.004% using a hybrid SBERT-FAISS and transformer classifier architecture with multilingual translation and human feedback.
VulGD is a dynamic open-access graph database that aggregates vulnerability data from multiple sources and uses LLM embeddings to enable more accurate risk assessment and threat prioritization.
citing papers explorer
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A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development
A within-subject study of 12 developers found that security training reduced validated weaknesses by 31.5% and critical issues by 79.2% in LLM-assisted backend coding.
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ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation
ORBIT achieves 100% compilation success and 91.7% test success on 24 mostly large programs from CRUST-Bench by using dependency-aware orchestration and iterative verification, outperforming prior static and baseline tools.
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SafeTrans: LLM-assisted Transpilation from C to Rust
SafeTrans achieves up to 80% successful C-to-Rust translations via LLM iterative repair on 2653 programs and two real projects, with some C vulnerabilities carrying over to the Rust output.
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Large Language Models as Explainable Cyberattack Detectors for Energy Industrial Control Systems
An off-the-shelf LLM prompted on tokenized Modbus traffic from public ICS datasets matches supervised baselines in normal-versus-critical classification accuracy while generating token-grounded audit records without any model updates.
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A Sociotechnical, Practitioner-Centered Approach to Technology Adoption in Cybersecurity Operations: An LLM Case
A six-month ethnographic co-creation project in a real SOC demonstrates that practitioner involvement in LLM tool design can overcome typical adoption barriers in cybersecurity operations.
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LanG -- A Governance-Aware Agentic AI Platform for Unified Security Operations
LanG presents a governance-aware agentic AI platform for unified security operations that reports strong performance on incident correlation, rule generation, attack reconstruction, and AI safety guardrails in an open-source package.
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SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training
SentinelSphere integrates an AI threat detector using an enhanced DNN on benchmark datasets with a fine-tuned quantized LLM for user training and awareness.
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Policy-Guided Threat Hunting: An LLM enabled Framework with Splunk SOC Triage
An integrated framework using autoencoders, deep reinforcement learning, and LLMs automates risk-based prioritization and contextual analysis of suspicious network traffic within Splunk SOC environments.
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Sentra-Guard: A Real-Time Multilingual Defense Against Adversarial LLM Prompts
Sentra-Guard reports 99.96% detection of adversarial LLM prompts with AUC 1.00 and ASR of 0.004% using a hybrid SBERT-FAISS and transformer classifier architecture with multilingual translation and human feedback.
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VulGD: A LLM-Powered Dynamic Open-Access Vulnerability Graph Database
VulGD is a dynamic open-access graph database that aggregates vulnerability data from multiple sources and uses LLM embeddings to enable more accurate risk assessment and threat prioritization.